1259 papers
Computational physics bridges the gap between abstract theory and real-world observation by using powerful computers to solve complex physical problems. This field allows scientists to simulate everything from the collision of subatomic particles to the swirling dynamics of galaxies, offering insights that traditional experiments alone cannot provide.
On Gist.Science, we continuously process every new preprint in this category from arXiv to make these breakthroughs accessible to everyone. Each entry is accompanied by both a clear, plain-language explanation and a detailed technical summary, ensuring that researchers and curious readers alike can grasp the significance of the latest findings without getting lost in dense equations.
Below are the latest papers in computational physics, curated to keep you at the forefront of this rapidly evolving discipline.
This paper presents a fast and accurate method for RFIC design space exploration that algebraically separates variant components from an invariant background to enable single-time background simulation and efficient component model fusion, significantly reducing computational costs while maintaining robustness.
This paper presents the first complete algorithm for Clebsch-Gordan tensor products in -equivariant neural networks that achieves a true asymptotic speedup from to by generalizing fast Fourier-based convolution to vector spherical harmonics and deriving a generalized Gaunt formula.
This study establishes a quantitative framework linking molecular geometry to collective lying-standing transition kinetics at organic-inorganic interfaces, demonstrating that diffusion-assisted mechanisms and footprint ratios drive emergent rate laws that cannot be predicted from single-molecule behaviors.
This paper reviews the hydrodynamic models of dense active fluids exhibiting turbulence-like states and introduces a theoretical framework where activity is treated as a dynamically advected field, revealing how spatial heterogeneity leads to sharp fronts, confined turbulence, and local, time-dependent universality in active systems.
The paper introduces MBD-ML, a pretrained message passing neural network that directly predicts atomic coefficients and polarizabilities from structures to enable efficient, accurate, and seamless integration of many-body dispersion interactions into various electronic structure codes and force fields without intermediate electronic calculations.
This study establishes the dynamic and mechanical stability of monolayer Y2TeO2 MOenes in both 1T and 2H phases, revealing their direct bandgaps and strong excitonic effects with binding energies up to 152 meV, which positions them as promising candidates for photovoltaic applications and low-dimensional many-body physics research.
This study utilizes high-pressure single-crystal X-ray diffraction with helium as a pressure medium to characterize the crystal structure and equation of state of ErVO4, revealing a zircon-to-scheelite transition at 7.9 GPa without phase coexistence or additional predicted transitions up to 24.1 GPa.
This paper argues that mutual-information area laws reveal the limitations of traditional block-spin renormalization group methods in two and three dimensions, explaining why tensor-network approaches succeed in 2D but face significant challenges in 3D due to the growth of entanglement entropy, a difficulty supported by numerical failures in estimating 3D Ising critical exponents.
This paper proposes a non-Markovian diffusion process derived from the Smoluchowski equation to model the memory effect between a semiflexible polymer system and its environment, demonstrating through numerical experiments on single-walled carbon nanotube collisions that heat diffusion compensates for correlated momentum in both equilibrium and far-from-equilibrium states.
This paper introduces a generalized characteristic mapping method that utilizes a semi-Lagrangian approach with Duhamel integrals to efficiently solve non-linear advection with source terms, demonstrating third-order accuracy and exceptional resolution in ideal magnetohydrodynamics simulations.